Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations505354
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.1 MiB
Average record size in memory104.0 B

Variable types

Numeric12
Categorical1

Alerts

Avg_Hillshade is highly overall correlated with Hillshade_Noon and 1 other fieldsHigh correlation
Elevation is highly overall correlated with Soil_Type and 1 other fieldsHigh correlation
Elevation_x_Slope is highly overall correlated with SlopeHigh correlation
Hillshade_Noon is highly overall correlated with Avg_HillshadeHigh correlation
Horizontal_Distance_To_Fire_Points is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Horizontal_Distance_To_Roadways is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Hydro_Road_Fire_Distance is highly overall correlated with Horizontal_Distance_To_Fire_Points and 1 other fieldsHigh correlation
Slope is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Soil_Type is highly overall correlated with ElevationHigh correlation
Wilderness_Area is highly overall correlated with ElevationHigh correlation
Horizontal_Distance_To_Hydrology has 21630 (4.3%) zeros Zeros

Reproduction

Analysis started2025-06-09 19:37:22.260637
Analysis finished2025-06-09 19:37:47.084567
Duration24.82 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

High correlation 

Distinct1978
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2951.5881
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:47.166906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2381
Q12814
median2987
Q33142
95-th percentile3322
Maximum3858
Range1999
Interquartile range (IQR)328

Descriptive statistics

Standard deviation275.48424
Coefficient of variation (CV)0.093334245
Kurtosis1.0931128
Mean2951.5881
Median Absolute Deviation (MAD)163
Skewness-0.89179409
Sum1.4915968 × 109
Variance75891.569
MonotonicityNot monotonic
2025-06-09T22:37:47.251195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2968 1625
 
0.3%
2962 1613
 
0.3%
2991 1613
 
0.3%
2972 1609
 
0.3%
2978 1603
 
0.3%
2975 1599
 
0.3%
2988 1554
 
0.3%
2955 1524
 
0.3%
2965 1519
 
0.3%
2952 1514
 
0.3%
Other values (1968) 489581
96.9%
ValueCountFrequency (%)
1859 1
 
< 0.1%
1860 1
 
< 0.1%
1861 1
 
< 0.1%
1863 1
 
< 0.1%
1866 1
 
< 0.1%
1867 1
 
< 0.1%
1868 1
 
< 0.1%
1871 3
< 0.1%
1872 4
< 0.1%
1873 1
 
< 0.1%
ValueCountFrequency (%)
3858 2
 
< 0.1%
3857 1
 
< 0.1%
3856 1
 
< 0.1%
3853 1
 
< 0.1%
3852 1
 
< 0.1%
3851 2
 
< 0.1%
3850 1
 
< 0.1%
3849 4
< 0.1%
3848 1
 
< 0.1%
3846 6
< 0.1%

Slope
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.767395
Minimum0
Maximum66
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:47.363706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4737982
Coefficient of variation (CV)0.5428622
Kurtosis0.75038989
Mean13.767395
Median Absolute Deviation (MAD)5
Skewness0.85685891
Sum6957408
Variance55.857659
MonotonicityNot monotonic
2025-06-09T22:37:47.447495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 30511
 
6.0%
11 30123
 
6.0%
12 29324
 
5.8%
9 29017
 
5.7%
13 28187
 
5.6%
8 27610
 
5.5%
14 25932
 
5.1%
15 24691
 
4.9%
7 24304
 
4.8%
6 22768
 
4.5%
Other values (57) 232887
46.1%
ValueCountFrequency (%)
0 621
 
0.1%
1 3485
 
0.7%
2 7301
 
1.4%
3 10951
 
2.2%
4 15310
3.0%
5 19417
3.8%
6 22768
4.5%
7 24304
4.8%
8 27610
5.5%
9 29017
5.7%
ValueCountFrequency (%)
66 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 2
 
< 0.1%
61 4
< 0.1%
60 2
 
< 0.1%
59 3
< 0.1%
58 1
 
< 0.1%
57 7
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

Zeros 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.15709
Minimum0
Maximum1397
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:47.527003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median216
Q3379
95-th percentile674
Maximum1397
Range1397
Interquartile range (IQR)271

Descriptive statistics

Standard deviation211.08873
Coefficient of variation (CV)0.79309826
Kurtosis1.6070835
Mean266.15709
Median Absolute Deviation (MAD)131
Skewness1.188776
Sum1.3450355 × 108
Variance44558.451
MonotonicityNot monotonic
2025-06-09T22:37:47.610232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 29993
 
5.9%
0 21630
 
4.3%
150 18274
 
3.6%
60 16778
 
3.3%
67 13454
 
2.7%
42 12960
 
2.6%
108 12702
 
2.5%
85 12156
 
2.4%
90 9757
 
1.9%
120 9343
 
1.8%
Other values (541) 348307
68.9%
ValueCountFrequency (%)
0 21630
4.3%
30 29993
5.9%
42 12960
2.6%
60 16778
3.3%
67 13454
2.7%
85 12156
2.4%
90 9757
 
1.9%
95 8120
 
1.6%
108 12702
2.5%
120 9343
 
1.8%
ValueCountFrequency (%)
1397 1
< 0.1%
1390 2
< 0.1%
1383 2
< 0.1%
1382 1
< 0.1%
1376 1
< 0.1%
1371 1
< 0.1%
1370 1
< 0.1%
1369 1
< 0.1%
1368 2
< 0.1%
1361 2
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ)

High correlation 

Distinct5785
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2431.98
Minimum0
Maximum7117
Zeros96
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:47.691141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile391
Q11140
median2078
Q33475
95-th percentile5571
Maximum7117
Range7117
Interquartile range (IQR)2335

Descriptive statistics

Standard deviation1600.4154
Coefficient of variation (CV)0.65807093
Kurtosis-0.53780115
Mean2431.98
Median Absolute Deviation (MAD)1085
Skewness0.65607631
Sum1.2290108 × 109
Variance2561329.4
MonotonicityNot monotonic
2025-06-09T22:37:47.772289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1078
 
0.2%
618 882
 
0.2%
900 821
 
0.2%
1020 805
 
0.2%
990 777
 
0.2%
960 764
 
0.2%
390 763
 
0.2%
1140 736
 
0.1%
1050 726
 
0.1%
750 725
 
0.1%
Other values (5775) 497277
98.4%
ValueCountFrequency (%)
0 96
 
< 0.1%
30 267
0.1%
42 153
 
< 0.1%
60 280
0.1%
67 249
< 0.1%
85 293
0.1%
90 331
0.1%
95 317
0.1%
108 537
0.1%
120 559
0.1%
ValueCountFrequency (%)
7117 1
< 0.1%
7116 1
< 0.1%
7112 1
< 0.1%
7097 1
< 0.1%
7092 1
< 0.1%
7087 2
< 0.1%
7082 1
< 0.1%
7079 1
< 0.1%
7078 2
< 0.1%
7069 1
< 0.1%

Hillshade_9am
Real number (ℝ)

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.28271
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:47.855569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1199
median218
Q3231
95-th percentile245
Maximum254
Range254
Interquartile range (IQR)32

Descriptive statistics

Standard deviation26.629387
Coefficient of variation (CV)0.12544303
Kurtosis2.1182007
Mean212.28271
Median Absolute Deviation (MAD)15
Skewness-1.2439666
Sum1.0727792 × 108
Variance709.12424
MonotonicityNot monotonic
2025-06-09T22:37:47.939574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 10413
 
2.1%
228 10242
 
2.0%
224 10051
 
2.0%
230 10049
 
2.0%
223 9782
 
1.9%
222 9720
 
1.9%
233 9416
 
1.9%
227 9382
 
1.9%
225 9243
 
1.8%
221 9239
 
1.8%
Other values (197) 407817
80.7%
ValueCountFrequency (%)
0 13
< 0.1%
36 1
 
< 0.1%
46 2
 
< 0.1%
50 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
54 3
 
< 0.1%
55 1
 
< 0.1%
56 5
 
< 0.1%
57 2
 
< 0.1%
ValueCountFrequency (%)
254 1641
 
0.3%
253 1822
 
0.4%
252 2123
0.4%
251 2409
0.5%
250 2766
0.5%
249 3116
0.6%
248 3249
0.6%
247 3691
0.7%
246 4069
0.8%
245 4560
0.9%

Hillshade_Noon
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.2563
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:48.022411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile187
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.58077
Coefficient of variation (CV)0.087705341
Kurtosis2.4443385
Mean223.2563
Median Absolute Deviation (MAD)12
Skewness-1.1404263
Sum1.1282347 × 108
Variance383.40657
MonotonicityNot monotonic
2025-06-09T22:37:48.107563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231 12314
 
2.4%
228 12230
 
2.4%
233 12063
 
2.4%
230 11975
 
2.4%
229 11849
 
2.3%
234 11805
 
2.3%
227 11601
 
2.3%
226 11594
 
2.3%
223 11564
 
2.3%
225 11495
 
2.3%
Other values (175) 386864
76.6%
ValueCountFrequency (%)
0 5
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
45 1
 
< 0.1%
53 2
 
< 0.1%
63 1
 
< 0.1%
64 1
 
< 0.1%
68 1
 
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
254 3981
0.8%
253 4664
0.9%
252 5492
1.1%
251 5864
1.2%
250 6488
1.3%
249 6356
1.3%
248 6907
1.4%
247 7617
1.5%
246 7453
1.5%
245 7352
1.5%

Horizontal_Distance_To_Fire_Points
Real number (ℝ)

High correlation 

Distinct5827
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.1361
Minimum0
Maximum7173
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:48.208832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile408
Q11024
median1725
Q32592
95-th percentile5089
Maximum7173
Range7173
Interquartile range (IQR)1568

Descriptive statistics

Standard deviation1357.8179
Coefficient of variation (CV)0.67548557
Kurtosis1.5080032
Mean2010.1361
Median Absolute Deviation (MAD)771
Skewness1.266097
Sum1.0158303 × 109
Variance1843669.5
MonotonicityNot monotonic
2025-06-09T22:37:48.297350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 1244
 
0.2%
541 981
 
0.2%
607 930
 
0.2%
942 856
 
0.2%
997 844
 
0.2%
700 833
 
0.2%
726 798
 
0.2%
752 782
 
0.2%
900 774
 
0.2%
960 768
 
0.2%
Other values (5817) 496544
98.3%
ValueCountFrequency (%)
0 45
 
< 0.1%
30 184
< 0.1%
42 183
< 0.1%
60 182
< 0.1%
67 370
0.1%
85 183
< 0.1%
90 182
< 0.1%
95 366
0.1%
108 369
0.1%
120 180
< 0.1%
ValueCountFrequency (%)
7173 1
< 0.1%
7172 1
< 0.1%
7168 1
< 0.1%
7150 1
< 0.1%
7145 1
< 0.1%
7142 1
< 0.1%
7141 2
< 0.1%
7140 1
< 0.1%
7131 1
< 0.1%
7126 1
< 0.1%

Avg_Hillshade
Real number (ℝ)

High correlation 

Distinct384
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.70605
Minimum31.666667
Maximum213.66667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:48.384865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31.666667
5-th percentile165.33333
Q1185.66667
median195.33333
Q3202.66667
95-th percentile211
Maximum213.66667
Range182
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.393874
Coefficient of variation (CV)0.074693421
Kurtosis3.0701699
Mean192.70605
Median Absolute Deviation (MAD)8.3333333
Skewness-1.3616708
Sum97384772
Variance207.18361
MonotonicityNot monotonic
2025-06-09T22:37:48.499882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199.3333333 6583
 
1.3%
198 6186
 
1.2%
198.6666667 6168
 
1.2%
201.3333333 5970
 
1.2%
196.3333333 5963
 
1.2%
200 5952
 
1.2%
195.6666667 5878
 
1.2%
195.3333333 5798
 
1.1%
196.6666667 5781
 
1.1%
200.6666667 5696
 
1.1%
Other values (374) 445379
88.1%
ValueCountFrequency (%)
31.66666667 1
< 0.1%
34.33333333 1
< 0.1%
55.33333333 1
< 0.1%
59.66666667 1
< 0.1%
61.66666667 2
< 0.1%
63.66666667 1
< 0.1%
64.33333333 1
< 0.1%
70 1
< 0.1%
73.33333333 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
213.6666667 247
 
< 0.1%
213.3333333 1927
0.4%
213 2939
0.6%
212.6666667 3181
0.6%
212.3333333 3415
0.7%
212 3115
0.6%
211.6666667 3447
0.7%
211.3333333 3739
0.7%
211 3372
0.7%
210.6666667 4121
0.8%

Hydro_Road_Fire_Distance
Real number (ℝ)

High correlation 

Distinct12679
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4708.2733
Minimum108
Maximum13141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:48.588126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile1448
Q12800
median4270
Q36223
95-th percentile9595
Maximum13141
Range13033
Interquartile range (IQR)3423

Descriptive statistics

Standard deviation2478.0415
Coefficient of variation (CV)0.52631642
Kurtosis-0.029145393
Mean4708.2733
Median Absolute Deviation (MAD)1624
Skewness0.7353494
Sum2.3793447 × 109
Variance6140689.8
MonotonicityNot monotonic
2025-06-09T22:37:48.673388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2950 134
 
< 0.1%
3001 125
 
< 0.1%
2956 123
 
< 0.1%
2914 122
 
< 0.1%
5340 121
 
< 0.1%
3118 118
 
< 0.1%
3020 117
 
< 0.1%
3547 116
 
< 0.1%
3194 116
 
< 0.1%
2149 116
 
< 0.1%
Other values (12669) 504146
99.8%
ValueCountFrequency (%)
108 1
< 0.1%
115 1
< 0.1%
125 1
< 0.1%
150 2
< 0.1%
152 1
< 0.1%
157 1
< 0.1%
162 1
< 0.1%
164 1
< 0.1%
166 2
< 0.1%
180 1
< 0.1%
ValueCountFrequency (%)
13141 1
< 0.1%
13134 1
< 0.1%
13127 1
< 0.1%
13124 1
< 0.1%
13121 1
< 0.1%
13114 1
< 0.1%
13113 1
< 0.1%
13111 1
< 0.1%
13110 2
< 0.1%
13109 2
< 0.1%

Elevation_x_Slope
Real number (ℝ)

High correlation 

Distinct37825
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40100.483
Minimum0
Maximum204880
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:48.756702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11242.6
Q124696
median37308
Q352513
95-th percentile78532
Maximum204880
Range204880
Interquartile range (IQR)27817

Descriptive statistics

Standard deviation20984.36
Coefficient of variation (CV)0.52329443
Kurtosis0.91042054
Mean40100.483
Median Absolute Deviation (MAD)13658
Skewness0.80115176
Sum2.026494 × 1010
Variance4.4034335 × 108
MonotonicityNot monotonic
2025-06-09T22:37:48.835701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 621
 
0.1%
38376 163
 
< 0.1%
35508 159
 
< 0.1%
39312 157
 
< 0.1%
29520 154
 
< 0.1%
29780 146
 
< 0.1%
38844 146
 
< 0.1%
26748 143
 
< 0.1%
29590 143
 
< 0.1%
48960 142
 
< 0.1%
Other values (37815) 503380
99.6%
ValueCountFrequency (%)
0 621
0.1%
1929 1
 
< 0.1%
1940 1
 
< 0.1%
2012 1
 
< 0.1%
2088 2
 
< 0.1%
2100 1
 
< 0.1%
2103 1
 
< 0.1%
2121 1
 
< 0.1%
2122 1
 
< 0.1%
2129 1
 
< 0.1%
ValueCountFrequency (%)
204880 1
< 0.1%
203808 1
< 0.1%
201110 1
< 0.1%
195796 1
< 0.1%
193579 1
< 0.1%
189297 1
< 0.1%
185673 1
< 0.1%
184912 1
< 0.1%
179312 1
< 0.1%
177320 1
< 0.1%

Soil_Type
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.67163
Minimum0
Maximum39
Zeros3031
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:48.910220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q120
median28
Q330
95-th percentile37
Maximum39
Range39
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1508803
Coefficient of variation (CV)0.38657584
Kurtosis-0.28933434
Mean23.67163
Median Absolute Deviation (MAD)4
Skewness-0.76966211
Sum11962553
Variance83.738611
MonotonicityNot monotonic
2025-06-09T22:37:48.988474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
28 115174
22.8%
22 48686
9.6%
31 46440
9.2%
32 33897
 
6.7%
29 30170
 
6.0%
11 29971
 
5.9%
9 26929
 
5.3%
21 25682
 
5.1%
30 24253
 
4.8%
23 16959
 
3.4%
Other values (30) 107193
21.2%
ValueCountFrequency (%)
0 3031
 
0.6%
1 4918
 
1.0%
2 2531
 
0.5%
3 7619
 
1.5%
4 1597
 
0.3%
5 6575
 
1.3%
6 105
 
< 0.1%
7 179
 
< 0.1%
8 1147
 
0.2%
9 26929
5.3%
ValueCountFrequency (%)
39 6834
 
1.4%
38 10406
 
2.1%
37 13519
 
2.7%
36 298
 
0.1%
35 119
 
< 0.1%
34 1391
 
0.3%
33 706
 
0.1%
32 33897
6.7%
31 46440
9.2%
30 24253
4.8%

Wilderness_Area
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
260796 
2.0
199161 
3.0
36968 
1.0
 
8429

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 260796
51.6%
2.0 199161
39.4%
3.0 36968
 
7.3%
1.0 8429
 
1.7%

Length

2025-06-09T22:37:49.062599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:37:49.138602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 260796
51.6%
2.0 199161
39.4%
3.0 36968
 
7.3%
1.0 8429
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 766150
50.5%
. 505354
33.3%
2 199161
 
13.1%
3 36968
 
2.4%
1 8429
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 766150
50.5%
. 505354
33.3%
2 199161
 
13.1%
3 36968
 
2.4%
1 8429
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 766150
50.5%
. 505354
33.3%
2 199161
 
13.1%
3 36968
 
2.4%
1 8429
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 766150
50.5%
. 505354
33.3%
2 199161
 
13.1%
3 36968
 
2.4%
1 8429
 
0.6%

Cover_Type
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0589349
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:37:49.185719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3893957
Coefficient of variation (CV)0.67481283
Kurtosis4.9724135
Mean2.0589349
Median Absolute Deviation (MAD)0
Skewness2.2809236
Sum1040491
Variance1.9304204
MonotonicityNot monotonic
2025-06-09T22:37:49.235718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 254165
50.3%
1 178709
35.4%
3 28058
 
5.6%
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
ValueCountFrequency (%)
1 178709
35.4%
2 254165
50.3%
3 28058
 
5.6%
4 2747
 
0.5%
5 9292
 
1.8%
6 14851
 
2.9%
7 17532
 
3.5%
ValueCountFrequency (%)
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
3 28058
 
5.6%
2 254165
50.3%
1 178709
35.4%

Interactions

2025-06-09T22:37:44.714082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:29.405454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.754023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.136290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:33.553312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.929158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.279568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:37.653268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:39.106477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.513286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:41.910627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:43.290719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:44.828415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:29.537987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.864008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.261310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:33.669618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.042173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.395624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:37.772557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:39.221989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.634500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.030736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:43.415576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:44.938677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:29.641006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.970523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.381616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:33.782133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.147691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.509648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:37.892068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:39.339505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.749524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.141016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:43.529086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.055316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:29.749520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:31.087900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.505838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:33.897521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.261990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.627160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.012932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:39.458525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.867805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.261042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:43.650619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.166556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:29.858474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:31.199415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.624728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.010648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.378510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.745908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.134444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:39.581599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.987984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.381547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:43.773781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.280066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:29.961987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:31.316625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.740751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.123159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.490119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.855424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.248973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:39.700110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:41.101247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.492061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:43.888654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.397852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.073346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:31.425923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.853641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.234463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.598634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.967359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.374489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:39.815633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:41.210760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.603494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:44.003800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.514368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.186858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:31.548652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.972738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.351297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.714925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:37.087872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.499791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:39.933152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:41.331041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.721646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:44.125105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.628638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.301125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:31.664903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:33.089259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.465857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.829442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:37.196177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.625013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.049580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:41.444559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.838911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:44.237620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.742959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.413637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:31.783420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:33.199572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.581211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:35.946094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:37.310689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.744301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.164095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:41.560399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:42.954571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:44.361368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.852982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.525440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:31.898950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:33.319084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.693588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.057606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:37.422201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.862321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.277451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:41.673912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:43.063686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:44.476545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:45.970207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:30.639441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:32.021467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:33.438597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:34.814640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:36.168630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:37.542248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:38.985224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:40.399971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:41.793225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:43.177200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:37:44.595788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-09T22:37:49.288909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Avg_HillshadeCover_TypeElevationElevation_x_SlopeHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHydro_Road_Fire_DistanceSlopeSoil_TypeWilderness_Area
Avg_Hillshade1.000-0.0780.193-0.499-0.1940.9860.0180.0360.2190.158-0.5230.0220.138
Cover_Type-0.0781.000-0.4930.084-0.009-0.063-0.121-0.007-0.226-0.2230.167-0.2240.482
Elevation0.193-0.4931.000-0.0230.0260.1860.1290.2610.4170.368-0.1750.5360.543
Elevation_x_Slope-0.4990.084-0.0231.000-0.119-0.443-0.1320.057-0.155-0.1780.9840.1280.132
Hillshade_9am-0.194-0.0090.026-0.1191.000-0.0860.126-0.0390.0070.067-0.1250.0090.196
Hillshade_Noon0.986-0.0630.186-0.443-0.0861.0000.0200.0320.2090.152-0.4680.0200.143
Horizontal_Distance_To_Fire_Points0.018-0.1210.129-0.1320.1260.0201.0000.0740.3710.735-0.1690.0720.290
Horizontal_Distance_To_Hydrology0.036-0.0070.2610.057-0.0390.0320.0741.0000.0720.1540.0130.1960.106
Horizontal_Distance_To_Roadways0.219-0.2260.417-0.1550.0070.2090.3710.0721.0000.880-0.2280.1910.363
Hydro_Road_Fire_Distance0.158-0.2230.368-0.1780.0670.1520.7350.1540.8801.000-0.2470.1750.424
Slope-0.5230.167-0.1750.984-0.125-0.468-0.1690.013-0.228-0.2471.0000.0300.206
Soil_Type0.022-0.2240.5360.1280.0090.0200.0720.1960.1910.1750.0301.0000.459
Wilderness_Area0.1380.4820.5430.1320.1960.1430.2900.1060.3630.4240.2060.4591.000

Missing values

2025-06-09T22:37:46.068931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-09T22:37:46.366841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationSlopeHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsAvg_HillshadeHydro_Road_Fire_DistanceElevation_x_SlopeSoil_TypeWilderness_AreaCover_Type
02596.03.0258.0510.0221.0232.06279.0200.3337047.07788.028.00.05
12590.02.0212.0390.0220.0235.06225.0202.0006827.05180.028.00.05
22804.09.0268.03180.0234.0238.06121.0202.3339569.025236.011.00.02
32785.018.0242.03090.0238.0238.06211.0199.3339543.050130.029.00.02
42595.02.0153.0391.0220.0234.06172.0201.3336716.05190.028.00.05
52579.06.0300.067.0230.0237.06031.0202.3336398.015474.028.00.02
62606.07.0270.0633.0222.0225.06256.0195.0007159.018242.028.00.05
72605.04.0234.0573.0222.0230.06228.0198.6677035.010420.028.00.05
82617.09.0240.0666.0223.0221.06244.0192.3337150.023553.028.00.05
92612.010.0247.0636.0228.0219.06230.0190.3337113.026120.028.00.05
ElevationSlopeHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsAvg_HillshadeHydro_Road_Fire_DistanceElevation_x_SlopeSoil_TypeWilderness_AreaCover_Type
5053443190.012.0190.01597.0228.0214.01584.0186.3333371.038280.030.02.01
5053453183.016.0162.01595.0231.0205.01608.0179.3333365.050928.030.02.01
5053463175.017.0134.01593.0227.0203.01632.0178.0003359.053975.030.02.01
5053473169.015.0108.01591.0222.0205.01657.0180.3333356.047535.030.02.01
5053483164.014.085.01590.0224.0209.01681.0183.0003356.044296.030.02.01
5053493158.013.060.01590.0230.0211.01706.0184.0003356.041054.030.02.01
5053503151.013.030.01590.0233.0214.01731.0186.0003351.040963.030.02.01
5053513145.013.00.01591.0228.0213.01756.0185.6673347.040885.023.02.01
5053523140.016.00.01593.0221.0204.01781.0179.6673374.050240.023.02.01
5053533134.017.030.01595.0211.0200.01806.0177.0003431.053278.023.02.01